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Ship contour extraction is vital for extracting the geometric features of ships, providing comprehensive information essential for ship recognition. The main factors affecting the contour extraction performance are speckle noise and amplitude inhomogeneity, which can lead to over-segmentation and missed detection of ship edges. Polarimetric synthetic aperture radar (PolSAR) images contain rich target scattering information. Under different transmitting and receiving polarization, the amplitude and phase of pixels can be different, which provides the potential to meet the uniform requirement. This paper proposes a novel ship contour extraction framework from PolSAR images based on polarization modulation. Firstly, the image is partitioned into the foreground and background using a super-pixel unsupervised clustering approach. Subsequently, an optimization criterion for target amplitude modulation to achieve uniformity is designed. Finally, the ship's contour is extracted from the optimized image using an edge-detection operator and an adaptive edge extraction algorithm. Based on the contour, the geometric features of ships are extracted. Moreover, a PolSAR ship contour extraction dataset is established using Gaofen-3 PolSAR images, combined with expert knowledge and automatic identification system (AIS) data. With this dataset, we compare the accuracy of contour extraction and geometric features with state-of-the-art methods. The average errors of extracted length and width are reduced to 20.09 m and 8.96 m. The results demonstrate that the proposed method performs well in both accuracy and precision. [ABSTRACT FROM AUTHOR]

Recent improvements in artificial intelligence and computer vision make it possible to automatically detect abnormalities in medical images. Skin lesions are one broad class of them. There are types of lesions that cause skin cancer, again with several types. Melanoma is one of the deadliest types of skin cancer. Its early diagnosis is at utmost importance. The treatments are greatly aided with artificial intelligence by the quick and precise diagnosis of these conditions. The identification and delineation of boundaries inside skin lesions have shown promise when using the basic image processing approaches for edge detection. Further enhancements regarding edge detections are possible. In this paper, the use of fractional differentiation for improved edge detection is explored on the application of skin lesion detection. A framework based on fractional differential filters for edge detection in skin lesion images is proposed that can improve automatic detection rate of malignant melanoma. The derived images are used to enhance the input images. Obtained images then undergo a classification process based on deep learning. A well-studied dataset of HAM10000 is used in the experiments. The system achieves 81.04% accuracy with EfficientNet model using the proposed fractional derivative based enhancements whereas accuracies are around 77.94% when using original images. In almost all the experiments, the enhanced images improved the accuracy. The results show that the proposed method improves the recognition performance. [ABSTRACT FROM AUTHOR]

There is a currently a lack of large-area plasma sterilization devices that can intelligently identify the shape of a wound for automatic sterilization. For this reason, in this work, a plasma sterilization device with wound-edge recognition was developed using a field-programmable gate array (FPGA) and a high-performance image-processing platform to realize intelligent and precise sterilization of wounds. SOLIDWORKS was used to design the mechanical structure of the device, and it was manufactured using 3D printing. The device used an improvement of the traditional Sobel detection algorithm, which extends the detection of edges in only the x and y directions to eight directions (0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°), completing the wound-edge detection by adaptive thresholding. The device can be controlled according to different shapes of sterilization area to adjust the positioning of a single plasma-jet tube in the horizontal plane for two-dimensional movement; the distance between the plasma-jet tube and the surface of the object to be sterilized can be also adjusted in the vertical direction. In this way, motors are used to move the plasma jet and achieve automatic, efficient, and accurate plasma sterilization. It was found that a good sterilization effect could be achieved at both the culture-medium level and the biological-tissue level. The ideal sterilization parameters at the culture-medium level were a speed of 2 mm/s and a flow rate of 0.6 slm, while at the biological-tissue level, these values were 1 mm/s and 0.6 slm, respectively. ARTICLE HIGHLIGHTS: • Image processing combining eight-direction Sobel edge detection and a median-filtering adaptive threshold was used for wound-edge recognition. • A device with x, y, and z degrees of freedom was designed and 3D printed. • An FPGA-based sterilization platform combining edge-recognition detection, a three-degree-of-freedom kinematic device, and a plasma injector was built to precisely sterilize wounds. [ABSTRACT FROM AUTHOR]

We establish a sample‐ and data‐processing pipeline that allows for high‐throughput optical microscope measurement of porous films, provided they are sufficiently optically scattering. Here, self‐limiting electrospray deposition (SLED) is used to manufacture scattering films of different morphologies. This technique compensates for the scattering of the films through background subtraction of the reflection image with the transmission image. This process is implemented through a combination of an ImageJ and MATLAB data pipeline; the Canny edge‐detector is used as the image‐processing algorithm to identify the boundaries of the film. This process is verified against manually measured images; a comparative study between cross‐sectional scanning electron microscopy (where scattering effects are diminished) and optical microscopy also verifies that our optical microscopy technique can be used to consistently, non‐destructively measure film thickness regardless of film morphology. In addition, this technique can be used in combination with dense film measurements to measure film porosity. [ABSTRACT FROM AUTHOR]

It has been thirty years since Perona and Malik (PM) introduced the nonlinear diffusion equation in image processing and analysis. The problem's complexity was to find a suitable and adaptive diffusion function that smooths away noise or textures while preserving sharp edges of a sufficiently smooth intensity. This paper provides a new two-dimensional analysis of the PM diffusion equation to examine its behavior during scales and an explicit formula to select the right diffusion function adequately. In this context, we study the PM equation at the zero crossings of the first and second directional derivatives of a sufficiently smooth function in the gradient direction. [ABSTRACT FROM AUTHOR]

Ocean temperature profile information plays a key role in understanding the marine environment. The passive remote-sensing technique can provide sea surface temperature measurements over large areas. However, it is sensitive to the atmospheric environment and cannot provide seawater temperature profile information. The lidar technique is the only way to carry out seawater temperature profile measurements over large areas. However, it is insufficient for measuring speed, the receiving field, stability, spectral integrity, simple system structures, and so on. Therefore, we propose a Brillouin lidar method combining two iodine cells at different temperatures to realize temperature measurements, where one iodine cell is used as a filter to absorb the elastic scattering and the other as an edge detection discriminator to obtain the seawater temperature measurement. The system has a fast measurement speed, a large receiving field, a simple system structure, and high stability. The system feasibility was verified via principle simulation and real iodine absorption curve measurements. For an ocean temperature of [5 °C, 15 °C], a laser wavelength of 532.10495 nm was more appropriate, corresponding to the iodine pool temperature combinations of 50 °C and 78 °C. For an ocean temperature of [15 °C, 32 °C], a laser wavelength of 532.10518 nm was more appropriate, corresponding to the iodine cell temperature combinations of 60 °C and 78 °C. When the laser intensity reached a measurement precision of 1‰, the temperature could be predicted with an accuracy of up to 0.2 K. This work shows promise as a potential solution for seawater temperature profile measurement. [ABSTRACT FROM AUTHOR]

Shadows in remote sensing images contain crucial information about various features on the ground. In this study, a method for detecting building shadows in GF-2 images based on improved quick shift was proposed. First, six feature variables were constructed: first principal component (PC1), brightness component (I), normalized difference shadow index (NDSI), morphological shadow index (MSI), normalized difference water index (NDWI), and normalized difference vegetation index (NDVI). Then, the image was segmented to obtain homogeneous objects, which were then classified using a random forest model. Two improvements were added to the quick shift algorithm: using PC1, I, and MSI as input data instead of RGB images; and adding Canny edge constraints. Validation in six research areas yields Kappa coefficients of 0.928, 0.896, 0.89, 0.913, 0.879, and 0.909, confirming method feasibility. In addition, comparative experiments demonstrate its effectiveness and robustness across different land cover types while mitigating the segmentation scale effect. [ABSTRACT FROM AUTHOR]

Video processing is the most emergent area of image processing in Research and Development. This paper, we present some advanced technology interrelated to video and image processing, which will provide a super-resolution high-quality most prominent visible light video for better human visualization and background motions or gesticulations of the human brain. There are two approaches for increasing the current resolution level of an image. The first one is the improvement of spatial resolution by reducing the size of pixels through the techniques of sensor manufacturing by about 40 $ {\mathcal{M}}{\textrm{m}^2} $ M m 2 for a 0.35 $ {\mathcal{M}}\textrm{m} $ M m CMOS process and the second approach is the enhancement of chip size that also enhances the capacitance. In video processing, we mainly focus on object images, i.e. any form of signal processing in which the input is an image and the output is a photograph or a frame of video. So, it will be better to converge over techniques of digital image processing. In the proposed algorithm (MHI Simulink model), edge extraction is performed to find the precious area of MRI and CT-Scan images. Using FPGA, edge detection methods have been implemented over scanned images to get better accuracy. The accuracy of the proposed method has been compared with Prewitt and Sobel's edge detection techniques. The proposed method has given better accuracy than Prewitt and Sobel methods. Finally, this paper shows future directions for researchers to enhance the characteristics of digital image and video processing. [ABSTRACT FROM AUTHOR]

Multichannel meta-imaging, inspired by the parallel-processing capability of neuromorphic computing, offers considerable advancements in resolution enhancement and edge discrimination in imaging systems, extending even into the mid- to far-infrared spectrum. Currently typical multichannel infrared imaging systems consist of separating optical gratings or merging multi-cameras, which require complex circuit design and heavy power consumption, hindering the implementation of advanced human-eye-like imagers. Here, we present printable graphene plasmonic photodetector arrays driven by a ferroelectric superdomain for multichannel meta-infrared imaging with enhanced edge discrimination. The fabricated photodetectors exhibited multiple spectral responses with zero-bias operation by directly rescaling the ferroelectric superdomain instead of reconstructing the separated gratings. We also demonstrated enhanced and faster shape classification (98.1%) and edge detection (98.2%) using our multichannel infrared images compared with single-channel detectors. Our proof-of-concept photodetector arrays simplify multichannel infrared imaging systems and offer potential solutions in efficient edge detection in human-brain-type machine vision. Here, the authors report the realization of a multichannel mid-infrared imaging system based on zero-bias type-printed graphene plasmonic photodetector arrays on ferroelectric substrates, showing enhanced infrared image recognition and edge detection accuracy. [ABSTRACT FROM AUTHOR]

Remote sensing image classification plays a crucial role in the field of remote sensing interpretation. With the exponential growth of multi-source remote sensing data, accurately extracting target features and comprehending target attributes from complex images significantly impacts classification accuracy. To address these challenges, we propose a Canny edge-enhanced multi-level attention feature fusion network (CAF) for remote sensing image classification. The original image is specifically inputted into a convolutional network for the extraction of global features, while increasing the depth of the convolutional layer facilitates feature extraction at various levels. Additionally, to emphasize detailed target features, we employ the Canny operator for edge information extraction and utilize a convolution layer to capture deep edge features. Finally, by leveraging the Attentional Feature Fusion (AFF) network, we fuse global and detailed features to obtain more discriminative representations for scene classification tasks. The performance of our proposed method (CAF) is evaluated through experiments conducted across three openly accessible datasets for classifying scenes in remote sensing images: NWPU-RESISC45, UCM, and MSTAR. The experimental findings indicate that our approach based on incorporating edge detail information outperforms methods relying solely on global feature-based classifications. [ABSTRACT FROM AUTHOR]

Scanning ion conductance microscopy (SICM) enables the non-invasive three-dimensional imaging of live cells and other structures in physiological environments. However, when imaging complex samples, SICM faces challenges such as having a low temporal resolution during slow scanning and a reduced signal-to-noise ratio during fast scanning, making it difficult to simultaneously improve both temporal and spatial resolution. To address these issues, this paper proposes an algorithm for enhancing image resolution under high-speed scanning. Firstly, scanning images are preprocessed using a median filtering algorithm to remove the salt-and-pepper noise generated during high-speed scanning. Next, the Canny edge detection algorithm is employed to extract the edges of the image targets. To avoid blurring the edges, the new edge-directed interpolation (NEDI) algorithm is then used to fill the edges, while non-edge areas are filled using bilinear interpolation, thereby enhancing the image resolution. Finally, the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are used to analyze the imaging of articular chondrocytes. The results show that under a scanning speed of 480 nm/ms, the proposed algorithm improves the temporal resolution of imaging by 60% compared to traditional 2× resolution imaging, increases the peak signal-to-noise ratio of the scanning images by 7 dB, and achieves a structural similarity of 0.97. Therefore, the proposed algorithm effectively removes noise during high-speed scanning and improves the SICM scanning imaging resolution, thereby avoiding the reduction in temporal resolution when scanning larger resolution samples and effectively enhancing the performance of SICM scanning imaging. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Imaging (MRI) holds a pivotal position in contemporary medical diagnostics and the formulation of treatment strategies. At the same time, real-time processing of its massive data faces challenges. The current research introduces a highly efficient way to use IIR filters with Xilinx System Generator (XSG) to construct MRI image processing algorithms. explore Furthermore, focuses on the feasibility of utilizing XSG for implementing image processing algorithms in the context of MRI. In the initial stage of the process, MRI image processing algorithms are used, such as image compression, edge detection, and image fusion. These algorithms are then implemented using XSG, which provides a graphical design environment for FPGA programming. The performance of the implemented algorithms is evaluated through some prescribed performance measures and the results showed that the proposed algorithms exhibited superior performance in comparison to existing research across the majority of measures. FPGA implementation of such algorithms is then achieved with less complexity (1%-2%). The results demonstrate that system generator provides an efficient approach for implementing MRI image processing algorithms on FPGA kit, enabling real-time processing and high-quality results. [ABSTRACT FROM AUTHOR]

In the context of geological research, the delineation of structural lineaments beneath sedimentary basins is pivotal for understanding subsurface features. This study presents a comprehensive analysis of the Garoua sedimentary basin (GSB) utilizing the high-resolution SGG-UGM-2 model, which offers a refined depiction of the Earth's gravity field. The primary objective was to elucidate the structural lineaments underpinning the basin. To achieve this, a suite of advanced edge-detection filters was employed, including the analytical signal, the total horizontal gradient, a modified version of the tilt angle, the gradient amplitude's tilt angle, and the fast sigmoid function. Each method was meticulously applied to the gravity data covering the Garoua basin. The application of these techniques was critically evaluated, focusing on their effectiveness in enhancing the resolution and clarity of the detected structural features. The results indicated that the fast sigmoid function filter exhibited superior performance, delineating more distinct edges with a notable absence of fictitious anomalies. This filter demonstrated a unique capability in balancing the anomalies attributed to both shallow and deep sources, thereby providing a more precise localization of edge structures within the basin. The ensuing structural map, primarily oriented along the NW-SE and ENE-WSW axes, emerges as a crucial resource. It offers invaluable insights for further investigations into potential hydrocarbon accumulations and groundwater sources. By pinpointing favorable locations for the existence or emergence of these resources, this map enhances the efficiency of exploratory efforts in the Garoua basin. The findings underscore the efficacy of the fast sigmoid function filter in geological studies, particularly in the context of gravity data analysis. This approach not only refines the understanding of subsurface structures but also paves the way for more targeted and effective exploration strategies in sedimentary environments. [ABSTRACT FROM AUTHOR]

Remote video imagery using shoreline edge detection is widely used in coastal monitoring in order to acquire measurements of nearshore and swash features. Some of these systems are constrained by their long setup time, positioning requirements and considerable hardware costs. As such, there is a need for an autonomous low-cost system (~EUR 500), such as Timex cameras, that can be rapidly deployed in the field, while still producing the outcomes required for coastal monitoring. This research presents an assessment of the effect of the sampling strategy (time-lapse intervals) on the precision of shoreline detection for two low-cost cameras located in a remote coastal area in western Ireland, overlooking a dissipative beach–dune system. The analysis shows that RMSD in the detected shoreline is similar to other studies for sampling intervals ranging between 1 s and 30 s (i.e., RMSDmean for Camera 1 = 1.4 m and Camera 2 = 0.9 m), and an increase in the sampling interval from 1 s to 30 s had no significant adverse effect on the precision of shoreline detection. The research shows that depending on the intended use of the detected shorelines, the current standard of 1 s image sampling interval when using Timex cameras can be increased up to 30 s without any significant loss of accuracy. This positively impacts battery life and memory storage, making the systems more autonomous; for example, the battery life increased from ~10 days to ~100 days when the sampling interval was increased from 1 to 5 s. [ABSTRACT FROM AUTHOR]

Change detection, an important task in remote sensing image analysis, has been extensively studied in recent years. However, change detection still faces problems such as difficulty in detecting small targets and incomplete edge detection. Furthermore, pseudo changes such as seasonal changes can also lead to many false detections. In response to these challenges, we propose the Multi-scale Features Mutual Enhancement Network (MFMENet), a simple yet efficient network. MFMENet maximizes feature utilization through mutual guidance and supplementation, enhancing the detection capabilities for small targets and edges. First, we use a lightweight feature extraction network to extract features, which mitigates the information loss caused by continuous downsampling of an overly deep network structure. Then, we design a Context Adaptive Interaction Module (CAIM) to realize the complementarity of feature information at different levels. This facilitates shallow features in gaining more semantic information and deep features in acquiring more texture information, thereby enhancing the model's capability to capture more comprehensive edge features while effectively mitigating interference from pseudo changes. Finally, we introduce a Feature Aggregation Comparison Module (FACM), which uses a combination of aggregation and comparison methods to refine and enhance features. FACM can not only highlight the changed features but also retain more details, improving the model's detection ability of small targets and edge details. The full utilization of features and effective mutual enhancement of information ensure the improvement of MFMENet's performance in small target and edge detection. Extensive experiments on three publicly available datasets (LEVIR, DSIFN, and CDD) demonstrate that our approach achieves superior performance with fewer parameters compared to state-of-the-art methods in recent years. In comparison to these baseline methods, our proposed approach achieves improvements of 0.98%, 12.24%, and 2.03% in the IOU metric on the LEVIR, DSIFN, and CDD datasets, respectively, while utilizing only 1.1 M parameters. [ABSTRACT FROM AUTHOR]

Remote sensing image change detection (CD) is an important means in remote sensing data analysis tasks, which can help us understand the surface changes in high-resolution (HR) remote sensing images. Traditional pixel-based and object-based methods are only suitable for low- and medium-resolution images, and are still challenging for complex texture features and detailed image detail processing in HR images. At present, the method based on deep learning has problems such as inconsistent fusion and difficult model training in the combination of the difference feature information of the deep and shallow layers and the attention mechanism, which leads to errors in the distinction between the changing region and the invariant region, edge detection and small target detection. In order to solve the above problems of inconsistent fusions of feature information aggregation and attention mechanisms, and indistinguishable change areas, we propose a multi-scale feature fusion Siamese network based on attention mechanism (ABMFNet). To tackle the issues of inconsistent fusion and alignment difficulties when integrating multi-scale fusion and attention mechanisms, we introduce the attention-based multi-scale feature fusion module (AMFFM). This module not only addresses insufficient feature fusion and connection between different-scale feature layers, but also enables the model to automatically learn and prioritize important features or regions in the image. Additionally, we design the cross-scale fusion module (CFM) and the difference feature enhancement pyramid structure (DEFPN) to assist the AMFFM module in integrating differential information effectively. These modules bridge the spatial disparity between low-level and high-level features, ensuring efficient connection and fusion of spatial difference information. Furthermore, we enhance the representation and inference speed of the feature pyramid by incorporating a feature enhancement module (FEM) into DEFPN. Finally, the BICD dataset proposed by the laboratory and public datasets LEVIR-CD and BCDD are compared and tested. We use F1 score and MIoU values as evaluation metrics. For AMBMFNet, the F1 scores on the three datasets are 77.69%, 81.57%, and 77.91%, respectively, while the MIoU values are 84.65%, 85.84%, and 84.54%, respectively. The experimental results show that ABMFNet has better effectiveness and robustness. [ABSTRACT FROM AUTHOR]

As an important method of image processing, image differentiation enables edge detection of targets to achieve recognition of object features and information compression and the computation speed can be boosted by optical information techniques. Traditional optical image differentiation methods mainly rely on spatial spectrum filtering by using classical 4f system, while some work focus on 1D or single‐direction differentiation. It is only in recent years that the rapid development of metasurfaces has facilitated image differentiation methods. In this work, a Laplace operation device is demonstrated based on a silicon hollow brick dielectric resonant metasurface for incident light field. The optical transfer function (OTF) required by the optical Laplace operation can be obtained by exciting an angularly selective toroidal dipole (TD) resonance supported by the metasurface. This hollow silicon brick differentiator not only realizes 2D second‐order edge detection but also has a numerical aperture close to 0.4 and a broad working wavelength range >100 nm and can be directly integrated with imaging systems. Such metadevice may be potentially applied in the fields of optical sensing, microscopy, machine vision, biomedical imaging, etc. [ABSTRACT FROM AUTHOR]

This paper presents the design and analysis of high‐index dielectric metasurfaces based on Fano resonance for image processing applications. With the aim of optical edge detection, two different metasurface structures are proposed to create first‐ and second‐order derivative operators. The metasurfaces designed for second‐order derivative is a square lattice structure consisting of silicon cylinders surrounded by a silicon dioxide layer. Moreover, another metasurface with a square lattice structure composed of three different silicon cylinders embedded in a silicon dioxide layer is proposed to perform first‐order derivation. To achieve the best performance of the proposed metasurfaces in edge detection, the physical dimensions of the proposed structures are optimized by conducting parametric studies to reach the maximum available numerical aperture (NA)$({{\mathrm{NA}}})$. Consequently, the second‐order derivative metasurface attains a numerical aperture with a magnitude NA=sin(47.5∘)=0.7372${\mathrm{NA}} = \sin ({{{47.5}}^\circ }) = 0.7372$, while the first‐order derivative metasurface achieves a numerical aperture of NA=sin(66.5∘)=0.917${\mathrm{NA}} = \sin ({{{66.5}}^\circ }) = 0.917$. Finally, the applicability of the proposed structures in optical edge detection is verified in the analog domain. The proposed metasurfaces provide high spatial resolution, high efficiency, and low dependence on the horizon angle, which pave the way for high‐quality image processing applications. [ABSTRACT FROM AUTHOR]

This work pioneers a novel approach in image edge detection through the utilization of the generalized fractional operator. By harnessing the global attributes inherent in fractional derivatives, it aims to enhance the extraction of intricate edge details.This is accomplished by creating the mask by using fractional derivative and adapt the mask by another parameter, yielding compelling and informative edge representations, as validated by experimental results. This advancement not only augments computer vision and image analysis techniques but also holds promise for refining image processing methodologies. Future endeavors may explore its adaptability across diverse imaging domains like medical and satellite imagery, while integration into deep learning frameworks could elevate its potential for advanced feature extraction and deeper image understanding. Additionally, optimizing its computational efficiency would broaden its scope for real-time deployment in fields such as robotics and autonomous systems. [ABSTRACT FROM AUTHOR]

With the rapid advancements in information technology and communication technology, the utilization of moving image visual communication has become widespread across various domains. In this context, optical sensing technology plays a pivotal role due to its remarkable attributes including high speed, vast capacity, and minimal delay. Thus, this research aims to enhance the optical sensing effect and edge detection accuracy in moving image visual communication by leveraging AI recognition and an optical sensing image edge detection algorithm. For this purpose, an AI recognition algorithm is implemented to train the model to identify the objects present in the moving images, enabling the localization of target objects within the optical sensing images. Consequently, the edge information of the target object is extracted from the photosensing image by employing an edge detection algorithm. Subsequently, the edge information of the optical sensing image undergoes processing and optimization to enhance the accuracy of edge detection. Through experimental results, it is demonstrated that the proposed approach, which integrates AI recognition and an optical sensor image edge detection algorithm, effectively identifies and localizes target objects within the optical sensor image, consequently improving the accuracy of edge detection outcomes. Furthermore, in comparison to conventional methods, the proposed methodology exhibits superior performance in optical image edge detection. [ABSTRACT FROM AUTHOR]

With the development of tourism, it is necessary to understand and evaluate the status of tourist resources in forest parks in order to improve visitor experience and manage forest parks. This paper presents an embedded edge computing method, which uses optical imaging technology to monitor and evaluate tourism resources in forest parks. Select the appropriate optical imaging equipment and embed it into the forest park monitoring system. Then the optical image data of each corner of the forest park is collected by the equipment, and these data are preprocessed and analyzed. A computer vision algorithm is used to detect, extract and recognize tourist resources in optical images, such as scenic spots, plants and animals. By combining embedded edge computing technology with optical imaging, accurate and detailed evaluation results of forest park tourism resources are obtained. These results can provide valuable information to forest park managers to help them understand the utilization of tourism resources, hot spots and room for improvement, so as to enhance the tourism experience and sustainable development of forest parks. [ABSTRACT FROM AUTHOR]

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Tidal stream turbines (TSTs) harness the kinetic energy of tides to generate electricity by rotating the rotor. Biofouling will lead to an imbalance between the blades, resulting in imbalanced torque and voltage across the windings, ultimately polluting the grid. Therefore, rotor condition monitoring is of great significance for the stable operation of the system. Image-based attachment detection algorithms provide the advantage of visually displaying the location and area of faults. However, due to the limited availability of data from multiple machine types and environments, it is difficult to ensure the generalization of the network. Additionally, TST images degrade, resulting in reduced image gradients and making it challenging to extract edge and other features. In order to address the issue of limited data, a novel non-data-driven edge detection algorithm, indexed resemble-normal-line guidance detector (IRNLGD), is proposed for TST rotor attachment fault detection. Aiming to solve the problem of edge features being suppressed, IRNLGD introduces the concept of "indexed resemble-normal-line direction" and integrates multi-directional gradient information for edge determination. Real-image experiments demonstrate IRNLGD's effectiveness in detecting TST rotor edges and faults. Evaluation on public datasets shows the superior performance of our method in detecting fine edges in low-light images. [ABSTRACT FROM AUTHOR]

Detecting changes in multisource heterogeneous images is a great challenge for unsupervised change detection methods. Image-translation-based methods, which transform two images to be homogeneous for comparison, have become a mainstream approach. However, most of them primarily rely on information from unchanged regions, resulting in networks that cannot fully capture the connection between two heterogeneous representations. Moreover, the lack of a priori information and sufficient training data makes the training vulnerable to the interference of changed pixels. In this paper, we propose an edge-oriented generative adversarial network (EO-GAN) for change detection that indirectly translates images using edge information, which serves as a core and stable link between heterogeneous representations. The EO-GAN is composed of an edge extraction network and a reconstructive network. During the training process, we ensure that the edges extracted from heterogeneous images are as similar as possible through supplemented data based on superpixel segmentation. Experimental results on both heterogeneous and homogeneous datasets demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Concrete structures inevitably experience cracking, which is a common form of damage. If cracks are left undetected and allowed to worsen, catastrophic failures, with costly implications for human life and the economy, can occur. Traditional image processing techniques for crack detection and measurement have several limitations, which include complex parameter selection and restriction to measuring cracks in pixels, rather than more practical units of millimetres. This paper presents a three-stage approach that utilises deep learning and image processing for crack classification, segmentation and measurement. In the first two stages, custom CNN and U-Net models were employed for crack classification and segmentation. The final stage involved measuring crack width in millimetres by using a novel laser calibration method. The classification and segmentation models achieved 99.22% and 96.54% accuracy, respectively, while the mean absolute error observed for crack width measurement was 0.16 mm. The results demonstrate the adequacy of the developed crack detection and measurement method, and shows the developed deep learning and laser calibration method promotes safer, quicker inspections that are less prone to human error. The method's ability to measure cracks in millimetres provides a more insightful assessment of structural damage, which is, in comparison to traditional pixel-based measurement methods, a significant improvement for practical field applications. [ABSTRACT FROM AUTHOR]

One of the critical issues of image processing, defined as obtaining useful information from the image and improving the quality of the image, is edge detection. How edge detection performance will be affected by adding morphological operators to edge detection algorithms is among the issues that have not been fully resolved. In the study, Canny and Sobel edge detection algorithms were applied to different milling cutters used in machinability. Morphological operators were applied to the determined edges, and their effects on the edges were examined. Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR) values were used to compare the performances of edge detection algorithms. According to MSE and PSNR results, it was seen that the Canny algorithm gave better results than the Sobel algorithm. In addition, it was concluded that the images obtained as a result of the applied morphological operations provided better performance than the images that were not applied for both Canny and Sobel algorithms. [ABSTRACT FROM AUTHOR]

Edge detection is one of the challenging problems in image processing. Four different classical edge detection methods--Sobel, Prewitt, Roberts, and Canny--and type-1 and type-2 fuzzy logicbased edge detection methods are applied to analyze two separate datasets with various properties. The datasets are STARE which contains medical images of the retina and BIPED which contains images of the street. Furthermore, two separate hybrid fuzzy logic methods are implemented. The type-1 and type-2 fuzzy inference techniques are combined to produce the hybrid-1 and hybrid-2 approaches, using the "AND" and "OR" logic operators. We compare the simulation results for each technique using three different image quality metrics. These are Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), and Structural Similarity Index (SSIM). The type-2 fuzzy technique outperformed the hybrid-1 fuzzy method in visual quality metrics comparison, demonstrating superior blood vessel recognition on the STARE retinal image dataset--a dataset that more closely resembles the human visual system. Using the BIPED street image dataset, the hybrid-1 fuzzy approach outperformed the Roberts method. The hybrid-1 fuzzy technique showed good results in the second order for both kinds of datasets. Any data and general applications can take advantage of it. [ABSTRACT FROM AUTHOR]

An image processing-based Firebug Swarm Optimization (FSO) algorithm is developed in this article to detect maximum power point tracking (MPPT) under shadowing conditions on PV arrays. After capturing an image and applying image processing techniques, the proposed method can detect the shading portion of photovoltaic array (PV). The image processing results can then be used to modify the current rating in power equations. The Firebug swarm optimization (FSO) algorithm is metaheuristic algorithm that identifies and controls switching configurations under partial shading patterns by detecting the maximum global peak point (GPP) of output power generated by multiple configurations. This research is proposed with an image processing edge detection algorithm called the Otsu-Canny algorithm for detecting the partial shade on PV array. The FSO algorithm is used to rearrange the shaded photovoltaic array to significantly reduce the power and efficiency losses. MATLAB simulations were carried out using a 280 W PV panel to verify the efficiency of the proposed FSO MPPT technique with an image processing-based algorithm. The proposed FSO MPPT scheme's performance is compared to current cutting-edge MPPT methods. Under partial shading conditions, the proposed algorithm successfully detected the maximum peak point of a photovoltaic array, increasing power output and efficiency. [ABSTRACT FROM AUTHOR]

Neural style transfer is an algorithm that transfers the style of one image to another image and converts the style of the second image while preserving its content. In this paper, we propose a style transfer approach for sand painting generation based on convolutional neural networks. The proposed approach aims to improve sand painting generation via neural style transfer, which can address the problem of blurred objects. Furthermore, it can reduce background noise caused by neural style transfers. First, we segment the main objects from the content image. Subsequently, we perform close–open filtering operations on the content image to obtain smooth images. Subsequently, we perform Sobel edge detection to process the images and obtain edge maps. Based on these edge maps and the input style image, we perform neural style transfer to generate sand painting images. Finally, we integrate the generated images to obtain the final stylized sand painting image. The results show that the proposed approach yields good visual effects from sand paintings. Moreover, the proposed approach achieves better visual effects for sand painting than the previous method. [ABSTRACT FROM AUTHOR]

Developing an effective underwater object detection method is crucial for both the conservation and utilization of marine life. However, the complexity and unpredictability of underwater environments introduce numerous challenges into the field of underwater object detection. The exploration and monitoring of underwater environments present unique challenges due to factors such as poor visibility, Color distortion, and varying lighting conditions. There have been numerous automatic techniques devised to tackle these challenges. Deep learning algorithms like Faster R-CNN, YOLO, SSD and machine learning algorithms which include SVM, RF, K-NN, Naive Bayes etc., which can be implemented over a large number of underwater images to detect objects or marine organisms present within them. For preprocessing the images, several Image processing techniques have been developed like Color correction, Contrast enhancement, Noise reduction etc. to optimize the detection process. This research paper discusses many object detection approaches in underwater images that have been proposed by researchers in recent years and challenges that are being encountered. In this study, we examined the performance of various deep learning algorithms used by various researchers in their paper in the context of underwater object detection. [ABSTRACT FROM AUTHOR]

The thermal front in the oceanic system is believed to have a significant effect on biological activity. During an era of climate change, changes in heat regulation between the atmosphere and oceanic interior can alter the characteristics of this important feature. Using the simulation results of the 3D Regional Ocean Modelling System (ROMS), we identified the location of thermal fronts and determined their dynamic variability in the area between the southern Andaman Sea and northern Malacca Strait. The Single Image Edge Detection (SIED) algorithm was used to detect the thermal front from model-derived temperature. Results show that a thermal front occurred every year from 2002 to 2012 with the temperature gradient at the location of the front was 0.3 °C/km. Compared to the years affected by El Niño and negative Indian Ocean Dipole (IOD), the normal years (e.g., May 2003) show the presence of the thermal front at every selected depth (10, 25, 50, and 75 m), whereas El Niño and negative IOD during 2010 show the presence of the thermal front only at depth of 75 m due to greater warming, leading to the thermocline deepening and enhanced stratification. During May 2003, the thermal front was separated by cooler SST in the southern Andaman Sea and warmer SST in the northern Malacca Strait. The higher SST in the northern Malacca Strait was believed due to the besieged Malacca Strait, which trapped the heat and make it difficult to release while higher chlorophyll a in Malacca Strait is due to the freshwater conduit from nearby rivers (Klang, Langat, Perak, and Selangor). Furthermore, compared to the southern Andaman Sea, the chlorophyll a in the northern Malacca Strait is easier to reach the surface area due to the shallower thermocline, which allows nutrients in the area to reach the surface faster. [ABSTRACT FROM AUTHOR]

As we all know day by day the technology is getting better and better, the production of counterfeit currency has been rapidly increasing. The counterfeit currency problem is faced by almost all countries. Since the real economy is affected, it has affected the economy of the country. Even when the drastic step of demonetization was taken in 2016 to overcome counterfeit currency, this problem did not end. The only one solution for this problem for a common man is to detect the fake currency, by using the fake currency detector machine. These machines are used in banks and large scale business, but for small scale businesses or for a common man these machines are not affordable. There are lot of researches taking place on this matter by using deep learning, image processing and machine learning techniques. This paper gives the complete methodology of fake note detector machine, which is affordable even for a common man. By implementing the applications of image processing techniques we can find out whether the currency notes are fake or not. Image processing technique consists of a number of operations that can be performed on an image, some of which include image segmentation, edge detection, gray scale conversion, preprocessing etc. The proposed system will detect the counterfeit currency of new denominations by distinguishing each denomination based on its size and depending on the features of each currency the comparison takes place. Based on the features matched, it detects whether the currency is counterfeit or not. The system have advantages like simplicity, reliability and cost effective. Which is affordable by a common man since the common man is the one who will be effected most, when the counterfeit currency are circulated in the market because he has to pay the real value of that currency. [ABSTRACT FROM AUTHOR]

Weeds are one of the utmost damaging agricultural annoyers that have a major influence on crops. Weeds have the responsibility to get higher production costs due to the waste of crops and also have a major influence on the worldwide agricultural economy. The significance of such concern got motivation in the research community to explore the usage of technology for the detection of weeds at early stages that support farmers in agricultural fields. Some weed methods have been proposed for these fields; however, these algorithms still have challenges as they were implemented against controlled environments. Therefore, in this paper, a weed image analysis approach has been proposed for the system of weed classification. In this system, for preprocessing, a Homomorphic filter is exploited to diminish the environmental factors. While, for feature extraction, an adaptive feature extraction method is proposed that exploited edge detection. The proposed technique estimates the directions of the edges while accounting for non-maximum suppression. This method has several benefits, including its ease of use and ability to extend to other types of features. Typically, low-level details in the formof features are extracted to identify weeds, and additional techniques for detecting cultured weeds are utilized if necessary. In the processing of weed images, certain edges may be verified as a footstep function, and our technique may outperform other operators such as gradient operators. The relevant details are extracted to generate a feature vector that is further given to a classifier for weed identification. Finally, the features have been used in logistic regression for weed classification. The model was assessed against logistic regression that accurately identified different kinds of weed images in naturalistic domains. The proposed approach attained weighted average recognition of 98.5% against the weed images dataset. Hence, it is assumed that the proposed approach might help in the weed classification system to accurately identify narrow and broad weeds taken captured in real environments. [ABSTRACT FROM AUTHOR]

Field programmable gate array (FPGA) is an integrated circuit consisting of internal hardware blocks with programmable link connections for users to customize operations for a particular application. Link connections can be easily reprogrammed, allowing the FPGA to adapt to changes to the design or even support a new application throughout the department's uptime. One of the important tasks in image processing is image edge detection image, with computer aided, image recognition is concerned with the recognition and classification of objects in an image, so edge detection is an important tool. In this paper, we design filter for edge detection in image processing using FPGA kit. We analysis and implementation of algorithm for image processing on FPGA, load the code and run the results. Comparative analysis with images processed by MATLAB software. [ABSTRACT FROM AUTHOR]

Automation in Agriculture is essential to achieve a better quality harvest and alleviating the dependency on human workers. As the southern part of India is rich in spices cultivation, the work emphases on one of the spice which has got more medicinal value and commercial viability. The spice considered in this is nutmeg which is mostly cultivated in the mountain ranges of Kerala and Kanyakumari District. The identification of mature nutmeg in a large group is a bit time consuming task. The current fruit-picking methods takes a long time in fixing the clamp on the right fruit from the cluster of fruits, these leads to the cervical spondylitis problem. Recent developments in image processing and the extensive usage of autonomous platforms have provided the opportunity for fast and automatic harvesting machines. This paper proposes an image segmentation algorithm to identify the mature nutmeg. The datasets used are KAU Kochukudy, IISR Keralashree, Punnathanam and local clone. The matured fruit is identified by it's boundaries using the boundary edge detection algorithm. The color detection method and colour space method were considered. Boundary Edge Detection algorithm is focused on identifying the edges or boundaries present in an image. Color Detection Method relies on identifying objects based on their color characteristics. Colour Space method involves converting the image from RGB color space (Red, Green, Blue) to a different color space, which may be more effective for certain types of analysis. Colour space methods outperforms image segmentation algorithm in terms of identification of matured nutmeg images with 96% accuracy. The average elapsed time was 1.150 secs. Also, the processing time to identify the right matured fruit requires much lesser time than other methods. [ABSTRACT FROM AUTHOR]

This paper introduces an edge AI-based scene-specific object detection system for long-term traffic management, focusing on analyzing congestion and movement via cameras. It aims to balance fast processing and accuracy in traffic flow data analysis using edge computing. We adapt the YOLOv5 model, with four heads, to a scene-specific model that utilizes the fixed camera’s scene-specific properties. This model selectively detects objects based on scale by blocking nodes, ensuring only objects of certain sizes are identified. A decision module then selects the most suitable object detector for each scene, enhancing inference speed without significant accuracy loss, as demonstrated in our experiments. [ABSTRACT FROM AUTHOR]

This study investigates the use of artificial neural networks (ANN) employing the backpropagation method to identify flower types based on petal shapes. Android devices were utilized to capture flower images and transmit them directly to a server. Once a sufficient number of images were gathered, the training of the artificial neural network commenced. The flower images were processed to extract shape features using Sobel edge detection followed by thresholding. Subsequently, the data were normalized and fed into the ANN for training. Once the training was complete, the Android devices were capable of capturing new flower images and using the ANN to identify them. The findings of this research indicate that by using a single hidden layer with 35 hidden nodes, the system achieved a flower detection accuracy of 80%. [ABSTRACT FROM AUTHOR]

Optical metasurfaces performing analog image processing – such as spatial differentiation and edge detection – hold the potential to reduce processing times and power consumption, while avoiding bulky 4 F lens systems. However, current designs have been suffering from trade-offs between spatial resolution, throughput, polarization asymmetry, operational bandwidth, and isotropy. Here, we show that dispersion engineering provides an elegant way to design metasurfaces where all these critical metrics are simultaneously optimized. We experimentally demonstrate silicon metasurfaces performing isotropic and dual-polarization edge detection, with numerical apertures above 0.35 and spectral bandwidths of 35 nm around 1500 nm. Moreover, we introduce quantitative metrics to assess the efficiency of these devices. Thanks to the low loss nature and dual-polarization response, our metasurfaces feature large throughput efficiencies, approaching the theoretical maximum for a given NA. Our results pave the way for low-loss, high-efficiency and broadband optical computing and image processing with free-space metasurfaces. Here the authors demonstrate a path to design metasurfaces that perform broadband, high-NA, high-efficiency and dual-polarization edge detection without using bulky 4 f systems. This work introduces new approaches towards passive, ultra-compact optical computing and image processing. [ABSTRACT FROM AUTHOR]

Cracks indicates the real time deformity in concrete structures. It is characterized as discontinuity in terms of shape and size of the concrete structures. To ensure the structural health and safety, crack detection is an important task. The traditional methods of crack detection include visual introspection, ultrasonic and hand-held testing of crack. These methods require a high human intervention along with an experienced and skilled inspector. Moreover, these methods are subjective and time-consuming process which fails to identify the crack of the complex concrete structures properly. To overcome these issues, a GrabCut with improved Sobel has been proposed for automatic crack detection from the concrete structures. The proposed method works as a two-step model where cracks regions are segmented in the first step and a precise crack assessment is performed in the second step. Furthermore, to improve the efficacy of Sobel, the mask is modified with the aid of local variance of the image instead of using conventional mask of the filter. For the experimentation study, the images of self-prepared concrete sample have been acquired. The effectiveness of the proposed method has been compared with respect to various pre-existing methods like Sobel, Prewitt, Robert, LoG, Zero Cross, and Canny. The comparative qualitative result exhibits that the proposed method surpasses the outcomes of the other pre-existing methods. Additionally, for easy implementation and application point of view a web tool of the proposed method has been developed. The web tool can be utilised by the civil infrastructure maintenance agency and construction engineers in the task of structure maintenance. [ABSTRACT FROM AUTHOR]

Metal flat surface in-line surface defect detection is notoriously difficult due to obstacles such as high surface reflectivity, pseudo-defect interference, and random elastic deformation. This study evaluates the approach for detecting scratches on a metal surface in order to address a problem in the detection process. This paper proposes an improved Gauss-Laplace (LoG) operator combined with a deep learning technique for metal surface scratch identification in order to solve the difficulties that it is challenging to reduce noise and that the edges are unclear when utilizing existing edge detection algorithms. In the process of scratch identification, it is challenging to differentiate between the scratch edge and the interference edge. Therefore, local texture screening is utilized by deep learning techniques that evaluate and identify scratch edges and interference edges based on the local texture characteristics of scratches. Experiments have proven that by combining the improved LoG operator with a deep learning strategy, it is able to effectively detect image edges, distinguish between scratch edges and interference edges, and identify clear scratch information. Experiments based on the six categories of meta scratches indicate that the proposed method has achieved rolled-in crazing (100%), inclusion (94.4%), patches (100%), pitted (100%), rolled (100%), and scratches (100%), respectively. [ABSTRACT FROM AUTHOR]

In order to optimize the wood internal quality detection and evaluation system and improve the comprehensive utilization rate of wood, this paper invented a set of log internal defect detection and visualization system by using the ultrasonic dry coupling agent method. The detection and visualization analysis of internal log defects were realized through log specimen test. The main conclusions show that the accuracy, reliability and practicability of the system for detecting the internal defects of log specimens have been effectively verified. The system can make the edge of the detected image smooth by interpolation algorithm, and the edge detection algorithm can be used to detect and reflect the location of internal defects of logs accurately. The content mentioned above has good application value for meeting the requirement of increasing demand for wood resources and improving the automation level of wood nondestructive testing instruments. [ABSTRACT FROM AUTHOR]

In the present era, there are lots of advancements and initiatives that have been undertaken through image processing techniques and IoT (Internet of Things). Image processing has proven its valuable insights in various applications such as GIS, biomedical, security, satellite imaging, medicine, and personal image analysis. In the context of fracture detection, image improvements, feature segmentation, and feature extraction techniques are commonly implemented including in the IoT Environment. The lower long bone, hand bone, and elbow bones are the particular interest due to their high incidence of fractures. X-ray diagnosis is a common method of detecting bone fractures due to its rapid and widespread availability. X-ray imaging involves a small amount of ionizing radiation in each part of the body, which is then captured on a particular film or digital detector. X-ray images, though they may have limitations compared to other imaging modalities, provide sufficient quality for fracture detection. There are three points of motivation for this research i.e. First- ease of use of software for patients and reduce the time for doctors and patients by screening out straight forward, Second- to decrease human mistakes that can also occur from manually inspecting a massive dataset of X-ray images to become aware of fractured sections of bones in hospitals, third- use of IoT infrastructure to collecting images of X-Rays and performing processing on received data by which we can send some accurate information back to the patients. The research aims to develop an automated environment i.e IoT emulation Framework consisting of image pre-processing such as attainment of images, pre-post-processing, segment methods, feature extraction, fracture detection, and visualization. Feature Extraction algorithm includes, CLAHE object with the preferred clip limit 2.0, CLAHE to the grayscale image, Gaussian blur to overcome more noise, Canny side detection, Hough Transform for line detection, and the gradient magnitude to acquire binary edges varied out through IoT. The framework utilizes the Canny edge detection methodology and Sobel operator for image segmentation. In this heat maps of images are also observed, which provide accurate information from bone images through IoT. The proposed system illustrates extreme accuracy and effectiveness, as proved by the results acquired from numerous experiments. The automated labeling and detection of bone fractures through photo processing by way of IoT offer great potential for fast and correct diagnosis, contributing to successful treatment outcomes. [ABSTRACT FROM AUTHOR]

This study aimed to deal with three challenges: robustness, imperceptibility, and capacity in the image watermarking field. To reach a high capacity, a novel similarity-based edge detection algorithm was developed that finds more edge points than traditional techniques. The colored watermark image was created by inserting a randomly generated message on the edge points detected by this algorithm. To ensure robustness and imperceptibility, watermark and cover images were combined in the high-frequency subbands using Discrete Wavelet Transform and Singular Value Decomposition. In the watermarking stage, the watermark image was weighted by the adaptive scaling factor calculated by the standard deviation of the similarity image. According to the results, the proposed edge-based color image watermarking technique has achieved high payload capacity, imperceptibility, and robustness to all attacks. In addition, the highest performance values were obtained against rotation attack, to which sufficient robustness has not been reached in the related studies. [ABSTRACT FROM AUTHOR]

The new generation of 3D video is an international frontier research hotspot. However, the large amount of data and high complexity are core problems to be solved urgently in 3D video coding. The latest generation of video coding standard versatile video coding (VVC) adopts the quad-tree with nested multi-type tree (QTMT) partition structure, and the coding efficiency is much higher than other coding standards. However, the current research work undertaken for VVC is less for 3D video. In light of this context, we propose a fast coding unit (CU) decision algorithm based on the gray level co-occurrence matrix (GLCM) and Extra trees for the characteristics of the depth map in 3D video. In the first stage, we introduce an edge detection algorithm using GLCM to classify the CU in the depth map into smooth and complex edge blocks based on the extracted features. Subsequently, the extracted features from the CUs, classified as complex edge blocks in the first stage, are fed into the constructed Extra trees model to make a fast decision on the partition type of that CU and avoid calculating unnecessary rate-distortion cost. Experimental results show that the overall algorithm can effectively reduce the coding time by 36.27–51.98%, while the Bjøntegaard delta bit rate (BDBR) is only increased by 0.24% on average which is negligible, all reflecting the superior performance of our method. Moreover, our algorithm can effectively ensure video quality while saving much encoding time compared with other algorithms. [ABSTRACT FROM AUTHOR]

Due to the constrained processing capabilities of real-time detection techniques in remote sensing applications, it is often difficult to obtain detection results with high accuracy in practice. To address this problem, we introduce a new real-time anomaly detection algorithm for hyperspectral images called cloud–edge RX (CE-RX). The algorithm combines the advantages of cloud and edge computing. During the data acquisition process, the edge performs real-time detection on the data just captured to obtain a coarse result and find the suspicious anomalies. At regular intervals, the suspicious anomalies are sent to the cloud for further detection with a highly accurate algorithm, then the cloud sends back the (high-accuracy) results to the edge for information updating. After receiving the results from the cloud, the edge updates the information of the detector in the real-time algorithm to improve the detection accuracy of the next acquired piece of data. Our experimental results demonstrate that the proposed cloud–edge collaborative algorithm can obtain more accurate results than existing real-time detection algorithms. [ABSTRACT FROM AUTHOR]

Magnetic and gravity anomalies have spatially overlapping fingerprints from many buried sources that differ in shape, depth, density contrast, magnetization intensity, and direction. Geophysicists have developed a suite of image enhancement filter algorithms that accurately represent the geometry and detail of subsurface features. Edge enhancement filters are high-pass filters that emphasize potential field anomalies, horizontal lateral edges, and the horizontal location of buried sources, i.e., specific combinations of directional derivatives of gravity and magnetic fields. Lateral edge enhancement filters (e.g., THG, AS, TA, TM, LTHG, IL, and ILTHG) were investigated using Gaussian noise on synthetic magnetic and gravity field data. The results show that LTHG and IL perform better than the other procedures. The ILTHG filter defined with the logistic function does not have the required accuracy and capability to determine the lateral boundaries. In addition, the filters were examined using real gravity field data from the Western Carpathians area in Slovakia. The primary and secondary faults in the western and southern Tribeč Mountains and the secondary faults and geological formations in the Pohronský Inovec Mountains are recognizable in the LTHG and IL images. The results of the LTHG and IL maps will allow us to improve the qualitative interpretation of gravity anomalies in studying the structural and tectonic geology of the Slovak Tribeč and Pohronský Inovec Mountains. [ABSTRACT FROM AUTHOR]

Magnetic data play a significant role in the interpretation of various geologic structures using depth estimation methods and edge detection filters. In this study, we applied methods based on directional derivatives, such as tilt-depth (TD), signum transform (ST), source distance (SD), and classical Euler deconvolution (ED) to estimate the depth of the magnetic sources. Moreover, to enhance the edges of magnetic field anomalies, we utilized the total horizontal derivative (THD), analytical signal (AS), tilt angle (TA), theta map (TM), hyperbolic tilt angle (HTA), the tilt angle of horizontal derivative (TAHG), and logistic function of total horizontal gradient (LTHD). These algorithms were tested on a synthetic magnetic model with noise and noise-free data to understand their performance. Since the edge detection filters are sensitive to noise, we carried out an upward continuation (UC) filter before the reduction of data to the magnetic pole to reduce the noise effect. After comparing the efficiency of the approaches in estimating the depth and horizontal lateral boundaries, these methods were applied to the data surveyed from the Aji-Chai salt dome in East Azerbaijan Province, Iran. The results obtained from the depth determination methods were compared with the drilling report from Iran's geological survey and the three-dimensional classical Euler deconvolution method. [ABSTRACT FROM AUTHOR]